The automatic recognition algorithm for surface defects of ceramic insulators on transmission towers based on deformable U-Net network

Wenqi Huang; Zhuojun Cai; Ruiye Zhou; Qunsheng Zeng; Yang Wu; Lingyu Liang; Jianing Shang; Xuanang Li

doi:10.1117/12.3008040

14 November 2023 The automatic recognition algorithm for surface defects of ceramic insulators on transmission towers based on deformable U-Net network

Wenqi Huang, Zhuojun Cai, Ruiye Zhou, Qunsheng Zeng, Yang Wu, Lingyu Liang, Jianing Shang, Xuanang Li

Author Affiliations +

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129341P (2023) https://doi.org/10.1117/12.3008040
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

To address the issues of low efficiency, insufficient accuracy, and high miss rate in traditional inspection methods for surface defects on ceramic insulators of transmission towers, this paper introduces a UAV-based intelligent inspection solution based on the deformable U-Net network to effectively detect and recognize surface defects on ceramic insulators in transmission towers. By using the deformable convolution operator to optimize the U-net network's convolution layer, the perceptual range of the convolution kernel is extended to improve the integrity of defect detail information. Meanwhile, the full-scale skip connection model is used to integrate high-dimensional and low-dimensional feature information to further improve the accuracy of ceramic insulator surface defect feature recognition. The experimental results show that the UAV-based intelligent inspection solution based on the deformable U-Net network can achieve an identification accuracy of 97.5%, an average precision of 95.55%, and an average intersection over union (IOU) of 91.67% in ceramic insulator surface defect detection. Compared with the traditional U-net method, the proposed solution in this study has improved the ceramic insulator surface defect inspection accuracy by 7.6%.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenqi Huang, Zhuojun Cai, Ruiye Zhou, Qunsheng Zeng, Yang Wu, Lingyu Liang, Jianing Shang, and Xuanang Li "The automatic recognition algorithm for surface defects of ceramic insulators on transmission towers based on deformable U-Net network", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129341P (14 November 2023); https://doi.org/10.1117/12.3008040

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KEYWORDS

Dielectrics

Deformation

Ceramics

Deep learning

Unmanned aerial vehicles

Education and training

Inspection

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